About the course
Tackle all CNN-related queries with this fast-paced guide
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and more. You will learn to create innovative solutions around image and video analytics to solve complex machine learning- and computer vision-related problems and implement real-life CNN models. This course starts with an overview of deep neural networks using image classification as an example and walks you through building your first CNN: a human face detector. You will learn to use concepts such as transfer learning with CNN and auto-encoders to build very powerful models, even when little-supervised training data for labeled images is available. Later we build upon this to build advanced vision-related algorithms for object detection, instance segmentation, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this course, you should be ready to implement advanced, effective, and efficient CNN models professionally or personally, by working on a complex image and video datasets.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Practical-Convolutional-Neural-Networks-Video-
Style and Approach
An easy-to-follow, concise and illustrative guide explaining core ConvNet concepts to help you understand, implement and deploy your CNN models quickly. The course has theoretical content for research and algorithms and the practical parts are implemented in code.
What You Will Learn
- From CNN basic building blocks to advanced concepts, understand practical areas they can be applied to
- Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
- Learn different algorithms that can be applied to object detection and instance segmentation
- Learn advanced concepts (such as attention mechanisms for CNN) to improve prediction accuracy
- Understand transfer learning and implement award-winning CNN architectures such as VGG, ResNet, and more
- Understand how generative adversarial networks work and how they can create new, unseen images
Md. Rezaul Karim
Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Center for Data Analytics, Ireland. He was a lead engineer at Samsung Electronics, Korea. He has 9 years' R&D experience with C++, Java, R, Scala, and Python. He has published research papers on bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, Deeplearning4j, MXNet, and H2O.
Mohit Sewak is an Artificial Intelligence scientist with extensive experience and technical leadership in research, architecture, and solutioning of Artificial Intelligence-driven cognitive and automation products and platforms for industries such as IoT, retail, BFSI, and cyber security.
In his current role at QiO Technologies, Mohit leads the reinforcement learning initiative for Industry 4.0 and Smart IoT.
In his previous role, Mohit was associated with IBM Watson Commerce (Software Group) where he led the research/science initiatives for the Watson Cognitive Commerce line of product features and offerings.
Mohit has been the Lead Data Scientist/Analytics Architect for some of the most renowned industry-leading International AI/ DL/ ML software and industry solutions. Mohit is also a thought leader in the field of Artificial Intelligence and Machine Learning and has authored multiple books and scientific publications in this area..
Pradeep Pujari is a machine learning engineer at Walmart Labs and a distinguished member of ACM. His core domain expertise is in information retrieval, machine learning, and Natural Language Processing. In his free time, he loves exploring AI technologies, reading, and mentoring
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